Physical Sciences and Mathematics Commons^™

Precision Agriculture In The Age Of Ai: A Systematic Review Of Machine Learning Methods For Crop Disease Detection, Munir Majdalawieh, Carla Martins, Mohammed Radi, Maher Alaraj, Shafaq Khan

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Artificial Intelligence (AI) has become a critical tool in modern precision agriculture, particularly in the detection of plant diseases and pests. This study provides a comprehensive review of current AI methodologies applied to crop disease detection, with a focus on machine learning models, dataset availability, and performance metrics. Our findings indicate that Convolutional Neural Networks (CNNs) are the most widely used and cost-effective approach, while Vision Transformers (ViTs) exhibit superior accuracy but require significantly higher computational resources. We identify key research gaps, including the geographic bias in dataset origins, the trade-off between data quality and quantity, and the limited exploration …

Reinforcement Learning Based Intelligent Optimisation For Bin Packing Problems: A Review, Nadia Dahmani, Amril Nazir, Ikbal Taleb, Syed M.Salman Bukhari

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The convergence of Reinforcement Learning (RL) and Bin Packing Problems (BPP) is a critical field of study that has profound ramifications in logistics, manufacturing, computer, and retail industries. This paper thoroughly examines the progression from simple rule-based tactics to advanced Deep Reinforcement Learning (DRL) techniques in solving BPPs. By conducting a thorough review of 231 papers conducted between 2019 and 2024, we address and provide answers to important research inquiries, such as “To what extent has academic research explored the use of RL for BPP during this time frame?” and “Which specific areas of application and methodologies have been predominantly …

Artificial Intelligence In Waste Management Systems: Applications, Challenges, And Prospects, Imane Belyamani

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Despite global recognition of the climate crisis, greenhouse gas emissions are projected to rise by 8.8 % by 2030, primarily due to inadequate planning, poor implementation, and insufficient financial support. While international initiatives such as the ’Waste to Zero’ coalition launched at the 28th Conference of the Parties to the UNFCCC (COP 28) highlight the urgency of advancing decarbonization and the circularity of waste systems, this review focuses on how artificial intelligence (AI) can accelerate that transformation. It systematically explores the role of AI in advancing waste management practices, with a focus on predictive analytics, route optimization, and machine learning-based …

Spatial–Temporal Deep Learning For Electric-Vehicle Charging Demand: An Exploratory Study Of Graph Convolutional And Lstm Networks Performance, Maher Alaraj, Carla Martins, Mohammed Radi, Mohamed Darwish, Munir Majdalawieh

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Electric-vehicle (EV) charging is a localized, time-varying load that challenges distribution networks. This study offers practical insights into when spatial graph structure adds value beyond temporal context, utilizing real-world data and a transparent evaluation. We compare Long Short-Term Memory (LSTM) and Graph Convolutional Network (GCN) models for hourly EV-charging energy forecasting, based on 145,778 sessions recorded in Boulder, Colorado (2018–2023). After preprocessing and temporal alignment, temporal covariates (hour, day, month, year) and, when applicable, ZIP-code indicators were engineered. LSTMs were trained with 1 h and 24 h input windows, with or without ZIP features, and evaluated through 5-fold cross-validation. GCNs …

Retaining The Herd: Habitat Restoration Efforts Increase Patch Retention Times In Mule Deer, Jack Rasmussen

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Across much of their range, and the State of Utah, mule deer (Odocoileus hemionus) populations have been declining. Declines in herd numbers can be attributed to alterations to historic habitats through the introduction of exotic annual grasses and the encroachment of pinyon-juniper forests on sagebrush ecosystems, changing resource availability. To combat habitat alterations, the Utah Department of Natural Resources has implemented various habitat restoration treatments through the Watershed Restoration Initiative , broadly to remove exotic species and decrease pinyon juniper woodland encroachment. We investigated how mule deer patch retention times differ on eight different habitat restoration treatment types during summer …

Fall 2024 Computer Programming And Engineering Self-Efficacy Survey Data, Mary Benjamin

Michigan Tech Research Data

This dataset was collected as part of a research study examining the impact of automated code critiquers on students’ programming and engineering self-efficacy in first-year engineering courses. The study involved pre- and post-surveys administered to students enrolled in ENG1101: Introduction to Engineering during Fall 2024 at Michigan Technological University. The research aims to understand how exposure to automated feedback tools, such as WebTA, influences confidence, persistence, and perceived competencies.

Data Supporting “High-Resolution Lidar Observations Of Sedimentation-Induced Size Sorting Of Droplets Near A Laboratory Cloud Top”, Fan Yang, Yong Meng Sua, Zipei Zheng, Jesse Anderson, Hamed F. Sadi, Jae Min Yeom, Suryadey P. Singh, Pei Hou, Will Cantrell, Ernie R. Lewis, Alexander Kostinski, Raymond Shaw

Michigan Tech Research Data

Cloud optical properties and precipitation, which are crucial to weather and climate, are strongly influenced by cloud microphysical properties that are still poorly understood. Here, we develop a high-resolution time-correlated single-photon-counting lidar and apply it to observe cloud microphysical properties at one-centimeter range resolution in a convection chamber under well-controlled conditions. Together with concurrent in-situ measurements and theoretical analysis, our lidar observations indicate that although turbulent mixing tends to homogenize the cloud in the bulk region, entrainment and sedimentation cause inhomogeneities in droplet concentrations near the cloud top. Specifically, the topmost region is directly affected by entrainment, and lidar profiles …

Culture Mediates Climate Opinion Change: A System Dynamics Model, Louis (Lou) Gross, Yoon Ah Shin, Sara M. Constantino, Ann Kinzig, Katherine Lacasse, Brian Beckage

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

A Novel Mathematical Model Of Oropouche Virus Transmission Dynamics, Darsh Gandhi, Carli Peterson, Emma Slack, Elizabeth Rubio, Amira Claxton, Christopher M. Kribs

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Modeling Social Isolation: A Data-Driven Index Function Design And Implementation, Jeremis N. Morales Morales, Carmen Caiseda, Phyllis Muniu, Joshua Atsu, Folashade B. Agusto

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Metapopulation Model For Oyster Restoration, Leah Shaw

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

[Chu] Modelling The Effect Of Disturbed Blood-Feeding On Mosquito-Borne Disease Transmission, Kyle Dahlin, Michael Robert, Lauren Childs

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Energy Input-Output Meta-Analysis Reveals Algal Diesel Struggles To Break Even (Supplemental Information), Michelle Arnold

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1. Algal biofuels have been investigated as an alternative to traditional fossil fuels for transportation in the United States since the 1970s. Yet after five decades of development, scalability and implementation remain limited. To evaluate the state of progress, this study harmonized energy inputs and outputs across 39 papers, encompassing 508 observations on the production of algal biofuel energy return on energy investment (EROEI) in the United States. The analysis produced a mean EROEI of 1.01—essentially the break-even point—irrespective of system boundaries. This is lower than bioethanol (2.8) and far below oil (8.7). Life-cycle analysis results showed that hydrothermal liquefaction in …

Increasing Complexity Leads To Declining Productivity Of Innovation In The Algal Biofuel Industry (Supplemental Information), Michelle Arnold

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Liquid biofuels are widely promoted as renewable, lower-emission alternatives to fossil fuels. Yet despite their promise, this sector faces a critical constraint: a long-term decline in the productivity of innovation. This decline—measured by a decreasing number of patents per inventor—suggests that generating technological breakthroughs is becoming more difficult, expensive, and uncertain. As innovation becomes more resource-intensive, the feasibility of replacing petroleum with liquid biofuels grows more tenuous. This is especially problematic given the pre-existing technical disadvantages of biofuels, including their relatively low energy return on energy investment (EROEI) and energy density compared to fossil fuels. Our findings show that the …

Data Accompanying "Landscape Composition Surrounding Restoration Projects Modulates Use By Wildlife: A Case Study Of Mule Deer In Utah", Jaylin Solberg

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With mule deer (Odocoileus hemionus), hereafter, deer, populations in decline, wildlife managers have increased their efforts to improve factors that limit population growth. In the State of Utah (hereafter, Utah), these efforts have included habitat restoration which is expected to improve environmental conditions for deer. Some of these efforts include pinon-juniper removal, prescribed burning, and spraying of invasive plant species. Although previous work has demonstrated the local impacts of restoration on deer populations, it is unknown how the environmental conditions in the landscape surrounding restoration treatments may modify local restoration impacts. Identifying how both local and landscape-scale conditions influence deer …

A New Scheme For Simple Asymmetric Bivariate Copulas And Applications, Rachid Bentoumi, Farid El Ktaibi, Christophe Chesneau

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Bivariate copulas play a central role in modeling the dependence structure between two random variables and serve as a fundamental tool in various applied fields. In this article, we develop a new theoretical framework aimed at constructing simple asymmetric bivariate copulas of the form C(u, v) = uv [ϕ(v) + u(1 − ϕ(v))^], (u, v) ∈ [0, 1]². This framework relies on a tuning univariate function to achieve the desired asymmetry. We study this pioneering scheme, emphasizing its theoretical foundations, and illustrating it with several examples. More precisely, we establish important properties of the proposed copulas …

Efficient Smooth Tensor Train And Tensor Ring Completion For Image Classification Enhancement, Salman Ahmadi-Asl, Roman V. Garaev, Rustam A. Lukmanov, Naeim Rezaeian, Asad Masood Khattak, Manuel Mazzara

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This paper deals with studying the data completion problem for enhancing the image classification task under the pixel removal scenario. In some applications, it happens that a part of the pixels of a given image is lost due to several issues, such as corruption by outliers or artifacts and/or incompleteness due to imprecise data acquisition. This issue results in a completely wrong classification outcome using Deep Neural Networks (DNNs). In this paper we investigate the benefit of data completion in enhancing the classification accuracy of the DNN models to build more robust and stable DNN models. To this end, we …

Reliability Targeted Snow Loads And Winter Wind Parameters For Locations Outside Of The Conterminous United States, Brennan L. Bean, Nicholas Brimhall, Bikram Bhusal, Marc Maguire, Maha Moussa

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The national building standard ASCE 7 moved to reliability-targeted snow loads (RTSLs) in the 2022 version. This necessitates the development of RTSLs for international locations. This repository contains the data and code needed to produce RTSLs and Winter Wind Parameters for locations outside of the Conterminous United States (OCONUS). It relies on annual maximum snow loads provided in a separate data release (see Brimhall et al. 2025).

Graphrag-Enabled Local Large Language Model For Gestational Diabetes Mellitus: Development Of A Proof-Of-Concept, Edmund Evangelista, Fathima Ruba, Salman Bukhari, Amril Nazir, Ravishankar Sharma

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Background: Gestational diabetes mellitus (GDM) is a prevalent chronic condition that affects maternal and fetal health outcomes worldwide, increasingly in underserved populations. While generative artificial intelligence (AI) and large language models (LLMs) have shown promise in health care, their application in GDM management remains underexplored. Objective: This study aimed to investigate whether retrieval-augmented generation techniques, when combined with knowledge graphs (KGs), could improve the contextual relevance and accuracy of AI-driven clinical decision support. For this, we developed and validated a graph-based retrieval-augmented generation (GraphRAG)–enabled local LLM as a clinical support tool for GDM management, assessing its performance against open-source LLM …

Chemical Analyses Of Grass Samples, Johan Du Toit, Shantell Garrett

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Results of chemical analyses of grass samples collected in the foothills of the Henry Mountains, south-central Utah, in the fall of 2021.

Plant Biomass (Dry) Per Square Meter, Johan Du Toit, Shantell Garrett

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Results of plant biomass clippings (dry mass / square meter) conducted in the foothills of the Henry Mountains, south-central Utah, over 2012-2022.

Chemical Analyses Of Soil Samples, Johan Du Toit, Shantell Garrett

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Results of chemical analyses of soil collected in the foothills of the Henry Mountains, south-central Utah, in the fall of 2020.

Lagomorph Spotlight Survey Results, Johan Du Toit, Shantell Garrett

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Results of lagomorph spotlight surveys conducted in the foothills of the Henry Mountains, south-central Utah, over 2014-2022.

Ecu-Malnett, Matthew G. Gaber, Mohiuddin Ahmed, Michael N. Johnstone

Research Datasets

ECU-MALNETT (ECU MALware NETwork Traffic) is a real world, reproducible dataset of labeled benign and malicious network flows built from the Peekaboo execution corpus. Peekaboo runs evasive malware with dynamic binary instrumentation and records raw host-level PCAPs while granting full Internet access, yielding noisy, real-world captures with background OS activity and concurrent processes. To derive trustworthy labels from these traces, we apply Construct, a baseline aware, zero-trust labeling framework. Construct first ingests a baseline capture to establish reference sets (DNS qnames, HTTP hosts, TLS SNIs, and socket endpoints) and grows a conservative benign IP pool only via whitelisted DNS resolutions. …

Dataset Of Raman Spectra And Matlab Code For: Elucidating Time-Resolved Intracellular Metabolic Dynamics Via Label-Free Raman Microspectroscopy And 2d Correlation Spectroscopy, Zohreh Mirveis, Nitin Patil, Hugh J. Byrne

Other Resources

This dataset contains raw spectral data obtained from single-cell Raman microspectroscopy under two nutritional conditions: glucose alone and glucose supplemented with glutamine. LLC-MK2 cells were starved for 2 h, then exposed to nutrients and fixed every 15 min for up to 120 min. At each time point, spectra were recorded from 25 individual cells (cytoplasm regions) and exported as machine-readable files (.csv). MATLAB scripts are provided to implement two-dimensional correlation spectroscopy (2D-COS) for generating synchronous maps, along with utilities for loading spectra, averaging, and reproducing key figures. A set of simulated time-series spectra used to validate 2D-COS under high background …

Data From: Nek4 Suppresses Cell Proliferation In Bt20 Triple-Negative Breast Cancer Cells By Diminishing Expression Of Cell Cycle Genes, While Its Depletion Mitigates Proliferation In Other Cell Lines, Amanda Ashley

Chemistry & Biochemistry: Datasets

No abstract provided.

Properties Of A Class Of Analytic Functions Associated With Exponentially Convex Functions, K. R. Karthikeyan, Elangho Umadevi, G. Thirupathi, Dharmaraj Mohankumar

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Studies in univalent function theory comprising the exponential of differential characterizations are rarely considered. The prominent study in this direction is the study of so-called α-exponentially convex functions. Here we study a class of analytic functions which satisfy an analytic characterization influenced by the definition of the multiplicative derivative and α-exponentially convex functions. Integral representation and coefficient inequalities of the defined function class are the main results of the paper.

Revised Draft Final Uniform Federal Policyquality Assurance Project Plan, Hydrogeologic, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Datasets Associated With Caballero Et Al. (2025): Sentinel-3 Coastal Analysis Ready Data (S3card): An Operational Framework For Coastal Water Applications, Cassia B. Caballero, Vitor S. Martins, Rejane S. Paulino, Thainara M. A. Lima, Elliott Butler, Eric Sparks

Research Data

This dataset provides access to twelve .zip archives containing pre-processed tiles from the S3CARD (Sentinel-3 Coastal Analysis Ready Data) framework. Each archive corresponds to a 2.5° × 2.5° coastal grid that includes an AERONET-OC (Aerosol Robotic Network – Ocean Color) station used for validation in Caballero et al. (2025). The data include atmospherically corrected, glint- and adjacency-corrected surface reflectance images derived from Sentinel-3 OLCI imagery.

Each tile includes:

- 21-band surface reflectance images in GeoTIFF format (valid water pixels only)

- Metadata files with acquisition geometry and processing details

These datasets support reproducibility and enable users to evaluate the S3CARD …

Fabrication, Calibration, And Deployment Of A Home-Made Radiometer, Callum Flowerday, Ryan Thalman, Jaron Hansen

ScholarsArchive Data

Subfolders and Contents:

Chamber Characterization: Contains emission spectrum measured from inside the chamber with converted spectra to power and photon flux.

LED Calibration: (Each calibration file contains folders of data from each LED used in calibration)

• Cone
• Cone 2
• Dome 2
• Flat Top
• Quartz Rod
• Uniform Dome

Outside:

• Cone 2 front porch
• Cone 2 roof
• Flat top